像素化沟通：可视化疾病风险地图和其他空间连续体中的不确定性
Pixelate to communicate: visualising uncertainty in maps of disease risk and other spatial continua
摘要：
长期以来，地图一直被用于可视化空间变量的估计，特别是疾病负担和风险。使用地质统计模型进行的预测具有通常在空间上变化的不确定性。然而，这种不确定性很难与估计数本身相对应，因此往往不被包括在内，从而可能产生对疾病负担或其他重要变量的确定性的误导感。为了弥补这一点，我们建议同时可视化预测和它们相关的不确定性在一个单一的地图通过不同的像素大小。我们用疟疾发病率的例子来说明我们的方法，但该方法可以应用于预测任何空间连续相关联的不确定性。
英文摘要：
Maps have long been been used to visualise estimates of spatial variables, in particular disease burden and risk. Predictions made using a geostatistical model have uncertainty that typically varies spatially. However, this uncertainty is difficult to map with the estimate itself and is often not included as a result, thereby generating a potentially misleading sense of certainty about disease burden or other important variables. To remedy this, we propose simultaneously visualising predictions and their associated uncertainty within a single map by varying pixel size. We illustrate our approach using examples of malaria incidence, but the method could be applied to predictions of any spatial continua with associated uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
